Speech-based searching of text or frequently asked questions (FAQs) databases provide users with a way to solve problems with various devices or appliances including e.g., cars, mobile phones, etc. Quite often, however, a search query results in finding an extensive list of answers or documents that are to be presented to the user. Such extensive lists may be caused either by ambiguity in the user's query or by combining multiple hypotheses about user's input yielded by input recognition systems such as, for example, automatic speech recognition (ASR) or handwriting recognition. These input recognition systems are not 100% reliable and, in most instances, they output several possible hypotheses.
Presenting such extensive lists of possible answers may be very distracting and/or tedious to a user. Further, in instances where the user is primarily engaged in or focused on another activity (e.g., driving a car), the user might not be able to browse such extensive lists of possible answers. One alternative to presenting an extensive list of all possible answers is to present an N-best list. However, even N-best lists may distract the user for the same reasons discussed above.
There have been a few attempts to alleviate the aforementioned problems. For example, both problems can be partly alleviated by having the user manually disambiguate the possible answers by refining his or her input query (e.g., rewording his or her search query). Additionally, a system may narrow down a user input query by selecting a particular ASR hypothesis from multiple ASR hypotheses. However, in some cases, such a process for disambiguation is not possible or satisfactory (e.g., when merging multiple ASR hypotheses together).